Jonas Wallin
Senior lecturer, Director of third cycle studies, Department of Statistics
Multivariate type G Matérn stochastic partial differential equation random fields
Author
Summary, in English
For many applications with multivariate data, random-field models capturing departures from Gaussianity within realizations are appropriate. For this reason, we formulate a new class of multivariate non-Gaussian models based on systems of stochastic partial differential equations with additive type G noise whose marginal covariance functions are of Matérn type. We consider four increasingly flexible constructions of the noise, where the first two are similar to existing copula-based models. In contrast with these, the last two constructions can model non-Gaussian spatial data without replicates. Computationally efficient methods for likelihood-based parameter estimation and probabilistic prediction are proposed, and the flexibility of the models suggested is illustrated by numerical examples and two statistical applications.
Department/s
- Department of Statistics
Publishing year
2020-02
Language
English
Pages
215-239
Publication/Series
Journal of the Royal Statistical Society. Series B: Statistical Methodology
Volume
82
Issue
1
Document type
Journal article
Publisher
Wiley-Blackwell
Topic
- Probability Theory and Statistics
Keywords
- Matérn covariances
- Multivariate random fields
- Non-Gaussian models
- Spatial statistics
- Stochastic partial differential equations
Status
Published
ISBN/ISSN/Other
- ISSN: 1369-7412